Report #2, Graduate Seminar Series, Fall 2014
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Summary Report of Computer Science Graduate
Seminar Presentations
Taha Hasan

Abstract—This report summarizes three presentations of the
Computer Science Graduate Seminars series. The topics covered
include supervised autonomy of robotic systems in complex
domains, image annotation and data mining for malware
detection.
Index Terms—material perception modeling, oblivious
computation, translucency, phase functions, multi-dimensional
scaling
I. INTRODUCTION
T
HIS report summarizes three presentations delivered as part
of the Graduate Seminar series in fall 2013. The
presentations concern supervised autonomy of robotic systems
in complex domains (Dr. Erion Plaku, Catholic University of
America), R-ontology for image annotation (Dr. Setareh
Rafatirad, George Mason University) and data mining for
malware detection (Dr. Yanfang Ye, West Virginia University)
and were delivered on 12/5, 11/21 and 11/14 respectively.
face detection, face recognition and scene understanding.
However, content-based recognition in a scene is incapable of
or limited in capturing context, higher level semantics. Dr.
Rafatirad’s work describes a comprehensive framework for
justifying increasingly complex inferences about the context of
an image. R-ontology is parent to the domain event ontology
that in turn groups events in the core-event model - described
as spatial, informational, experiential, causal, structural,
temporal etc. in Westermann and Jain [1] - using sequentially
larger contexts.
The domain event ontology is tasks, relationships and
domain rules. R-ontology describes multiple instances of an
event (event is either defined in the domain ontology or is
ranked closest to a sub-event for an existing event in the domain
ontology). Presented results for correctness of a particular
ontology in a tagging problem in Dr. Rafatirad’s work show that
increase in average correctness decreases the total number of
non-miscellaneous events. In addition, more context
information increases average correctness.
IV. DATA MINING FOR MALWARE DETECTION
II. SUPERVISED AUTONOMY OF ROBOTIC SYSTEMS
Dr. Erion Plaku’s work addresses supervised autonomy of
robotic systems working in complex environments, so as to
alleviate the burden placed on human supervisors, to provide
instructions and feedback to supervisor and to ensure safety and
predictability of performance in a manner similar to supervision
of humans. The core problem in this domain is the motion
planning with dynamics problem. The initial step uses
exhaustive search/dynamics simulation for the final
coordinated alongside collision checks. The following step uses
a discrete layer to guide the search: it uses workspace
decomposition to provide a discrete layer as an adjacency
graph. The work also features a supervisory task-level interface
with descriptions in natural, structured languages and the use of
linear temporal logic to streamline the interaction between
robotic systems and humans. Potential applications include
task-level interfaces for robotic swarms and multi-scale
planning for complex missions.
Dr. Ye’s work is at the intersection of malware detection and
big data analytics. Traditional signature based malware
detection relies on short strings of bytes unique to programs
and typically fails to detect variants of known malware.
Heuristics-based malware detection, on the other hand, is
mostly done through manual analysis by experts.
Distributed, cloud based malware detection reduces (to
milliseconds) the turn-around time compared to that of
traditional malware detection/classification. The use of modelpredictive methods enables the negligible time-window
needed for malware detection. Feature extraction for the
proposed strategy involves both static analysis (binary strings,
n-gram instructions, API calls) and dynamic analysis
(sequences of system calls). Association rules are mined to
associate the WinAPI calls contributing to malware detection;
map-reduce can be employed to parallelize the implementation
and ensemble methods can be used to boost predictive
performance [2].
V. QUESTIONS AND DISCUSSION
III. R-ONTOLOGY FOR IMAGE ANNOTATION
Image annotation is a critical problem associated with
applications such as concept recognition, celebrity recognition,
Taha Hasan is with the Department of Computer Science, Virginia Tech,
Northern Virginia Center, Falls Church VA 22043 (email: taha@vt.edu).
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Report #2, Graduate Seminar Series, Fall 2014
REFERENCES
[1]
[2]
Westermann, Utz, and Ramesh Jain. "Events in multimedia electronic
chronicles (e-chronicles)." International Journal on Semantic Web and
Information Systems (IJSWIS) 2.2 (2006): 1-23.
Ye, Yanfang, et al. "Automatic malware categorization using cluster
ensemble."Proceedings of the 16th ACM SIGKDD international
conference on Knowledge discovery and data mining. ACM, 2010.
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